Exploring motor imagery classification in brain-computer interfaces: Case study of the Vietnamese BCI speller

Recent advancements in Brain-Computer Interface technology have led to the development of BCI Spellers, providing innovative communication solutions, particularly for individuals with disabilities. This paper presents a Vietnamese BCI Speller system specifically designed for Vietnamese speakers, addressing the unique linguistic and contextual needs of this population. Our research focuses on two primary contributions: first, we evaluate and implement a motor imagery classification method using a scalable machine learning framework tailored for processing motor imagery signals; second, we propose a user-friendly BCI Speller that facilitates document drafting and communication at an affordable cost. We detail the integration of motor imagery task classification within our Vietnamese BCI Speller, employing both deep learning and traditional machine learning models. Experimental results demonstrate over 90% accuracy in classifying motor imagery tasks with Linear Discriminant Analysis and Common Spatial Parttern, highlighting the system's effectiveness and potential for enhancing user interaction. This study emphasizes the need for continued research to optimize BCI technology and improve accessibility for all users.